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In this paper, we present our approach for the 'Detection of Propaganda Techniques in News Articles' task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda spans in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.

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Summary Holistic understanding of well operations can play a key role in optimization and maintenance of assets. The traditional process for understanding well operations mainly involves fitting a curve through all of the historical production data and extending the curve to forecast production without modeling the stochastic nature of production history, considering impact of any well interventions, or feeding any a priori information into curve-fitting workflows. This leads to unreliable production and reserves estimates which, in turn, impact the strategy and planning process for asset management. A novel workflow was developed learns the production characteristics of a well through a statistical framework using principles of signal processing and Bayesian inferences. Using this workflow, high-fidelity empirical production performance forecasts can be obtained for all the wells in the asset in an automated fashion. This novel workflow aims to significantly simplify interpretation of well operations, reduce the turnaround time for analyzing and modeling well performance, and improve the quality of reserves estimates.